Semi-Automated Imagery Analysis and LiDAR …€¦ · 6 What is LiDAR? Light Detection And Ranging...

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Ministry of Natural Resources

Murray WoodsMurray WoodsOntario Ministry of Natural ResourcesOntario Ministry of Natural Resources

Southern Science and InformationSouthern Science and InformationNorth BayNorth Bay

[email protected]@Ontario.ca

SemiSemi--Automated Imagery AnalysisAutomated Imagery Analysisand LiDAR Enhancements toand LiDAR Enhancements toNatural Resource InventoriesNatural Resource Inventories

mailto:[email protected]

mailto:[email protected]

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AAdvanceddvanced FForestorest RResourceesource IInventorynventory TTechnologiesechnologies

Doug Pitt – Don Leckie – François Gougeon – Paul Treitz - Al Stinson – Murray Woods - Dave Nesbitt

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Project Objective• push “beyond” traditional forestinventory descriptors• leverage existing projects currentlyunderway• advance existing methods andapproaches• link multiple remote-sensing techniquesto derive products at multiple scales

(Tree → Stand → Landscape)

• move toward linking solid inventoryattributes and wood fibre characteristics• develop tools to transfer science &research efforts to government and theprivate sector• ‘operationalize’ the methods developed

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Project Focus

• Use of LiDAR technologies to enhance thescope and resolution of natural resourceinventories

• Improvement of semi-automated image analysistechniques to classify tree crowns and developtree/stand polygon inventories

• Use independently or fuse these technologies toimprove natural resource inventories.

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Explore the potential of:

1. Using LiDAR formeasurement andprediction of stand levelinventory metrics

2. Determine of appropriateacquisition intensities for arange of forest types

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What is LiDAR?What is LiDAR?LLiightght DDetectionetection AAndnd RRanginganging

• Active remote sensing technology

• Involves transmitting and receiving~150,000 pulses of laser light persecond

• Pulses strike the surface of theearth and with each pulse get ameasurement of the time and angleof each return

• If a laser pulse hits an objectthrough which it can penetrate, it willproduce range and intensitymeasurements for each surface it hitsresulting in multiple returns

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LiDAR Intro 1LiDAR Intro 1x,y,z

x,y,z+range

range

)/(__*2

)(_)( smlightofspeed

stimeelapsedmrange

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LiDAR Intro 2LiDAR Intro 2

x+x1,y,z+z1

z1

x1

θ

range

x,y,z

x,y,z+range

range

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Example of flight line RAW LiDAR stripExample of flight line RAW LiDAR strip

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Ground Hits OnlyGround Hits Only

NonNon--Ground Hits OnlyGround Hits Only

Classification of ReturnsClassification of Returns

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RGB

12

DSM

RGB

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DSM

DTM

RGB

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DSM

DTM

RGB

Profile

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Digital Surface Model (DSM)Digital Surface Model (DSM)

Digital Terrain Model (DTM)Digital Terrain Model (DTM)

Canopy Height Model (CHM)Canopy Height Model (CHM)

From St-Onge, B., Treitz, P., Wulder, M., Kurtz, W., Gillis,M. 2004. Retrospective mapping of structural andbiomass changes in forest ecosystems usingphotogrammetryand laser altimetry,AmercianGeophysical Union/Canadian Geophysical Union JointAssembly, Montreal, May 17-21.

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Quick and Easyproduction of raster-based data productsfrom lidar data..

• Digital surface models• Digital terrain models• Canopy height model• Point density grids• Canopy closure grids• Height structure index grids

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• Detailed Surface Models– Digital Surface Models (DSM)– Digital Terrain Models (DTM)– Canopy Height Models (CHM)

LiDAR’s Contribution to Precision Forest Inventory

DTMDTM

DSMDSM

CHMCHM

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LiDAR’s Contribution to Precision Forest Inventory• Detailed Surface Models

– Digital Surface Models– Digital Terrain Models– Canopy Height Models

• Detailed Digital Terrain Model– Supporting

• Identifying surficial geologyValue A

ddedV

alue Added

Eskers Dunes

Used with permission from Al Thorne, Tembec.Used with permission from Al Thorne, Tembec.

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Northern Ontario 20m DTMNorthern Ontario 20m DTM

LiDAR provides Improved Digital Terrain Models

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LiDAR derived 5m DTMLiDAR derived 5m DTM

LiDAR provides Improved Digital Terrain Models

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OBM 10m DEM 2.0

LiDAR 1m DEM

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• Identifying surficial Geology• Hydrological modellingV

alue Added

Value A

dded

Predictive Hydrology Models

Predictive Streams

Predicted Drainage requiring Culvert installationPredicted Drainage requiring Culvert installation

•• 86% reliable86% reliable predictions either right on or withinpredictions either right on or within 15 meters15 meters ofofpredicted streams.predicted streams.

•• 95% reliable95% reliable predictions either right on or withinpredictions either right on or within 35 meters35 meters ofofpredicted streams.predicted streams.

OBM Mapped Stream Locations areOBM Mapped Stream Locations are……““completely unreliable for block layout and water crossingscompletely unreliable for block layout and water crossings””

Mark Joran, Millson Forestry ServiceMark Joran, Millson Forestry Service

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• Identifying surficial Geology• Hydrological modelling• Wetland classification

Value A

ddedV

alue Added

Improved Predictive Wetland Classification in N.Improved Predictive Wetland Classification in N.Ontario when compared to current 20m DEMOntario when compared to current 20m DEM

•• ““LiDAR had problemsLiDAR had problemswith obtaining barewith obtaining bareearth returns in alderearth returns in alderthickets and cedarthickets and cedarwetlandswetlands””

Adam HoggAdam Hogg -- IMAIMA

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• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive ELC

Value

Value

Added

Added

Output from LandMapR

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• Detailed Surface Models– Digital Surface Models– Digital Terrain Models– Canopy Height Models


• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive ELC

Value

Value

Added

Added

LiDAR’s Contribution to Natural Resource Inventories

Predicted Ecosite MappingProviding additional information:• vegetation communities• Soil depth,• Soil texture• Soil moisture regime,• Soil nutrients• Landform•Geomorphology descriptors (sand,gravel, rock, fluvial, alluvial fans,lacustrine, glaciofluvial, etc…).

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• Identifying surficial Geology• Hydrological modelling• Wetland identification• Predictive Ecosystem mapping• Operational considerations

– road construction– skid trail layout– water crossings

Value A

ddedV

alue Added

Virtual Road layoutVirtual Road layout

Origin

Destination

DEM 2m

Profile from DEM

Wetness index and trail layoutWetness index and trail layout

FPInovations Ferric

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• Direct measurement of:• Stand/Tree Heights• Crown Closure

Value A

dded

1m Canopy Height Model1m Canopy Height Model

Canopy Closure Comparison

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6

Plot

0

10

20

30

40

50

60

70

80

90

100

Calculated Stand Top H

eight (m)

Lidar Maximum Vegetation Return vs. Calculated Top Height

0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Lidar Max Vegetation Return (m)

Mean - -0.79m

Min - -4.85m

Max - 4.07m

Std - 1.50m

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LiDAR’s Contribution to Precision Forest Inventory• Detailed Digital Surface Models






• Individual Tree Information– Tree crown centre– Tree crown radii– Tree height

Value A

ddedV

alue Added

ITC Suite – François GougeonTreeVaw - Sorin Popescu

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LiDAR’s Contribution to Precision Forest Inventory• Detailed Digital Surface Models






• Individual Tree Information

• Statistically based predictions ofstand attributes

Value A

ddedV

alue Added

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Lidar-basedmodels of

forestparameters

• Height• Volume (GTV, GMV)• Basal area• Density• Quadratic mean DBH• Biomass• Diameter and Basal

Area Distributions

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Canopy Height Model (CHM)

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LiDAR Predictors

• Statistical– Mean, Standard Deviation,

Absolute Deviation, Skew, Kurtosis

• Percentiles– Deciles (p10 … p90) and Maximum Height

• Density– d1 … d9

– Da : Number of first returns divided by all returns.

– Db : Number of first returns classified as non-ground divided by all returns.

• derived from First, or All returns; with or without a z threshold

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P0

heig

ht

1

q(ht)

q(ht)

Concept of Canopy Height Metrics

0

5

10

15

20

25

30

4.2966e+54.2967e+5

4.2968e+54.2969e+5

4.2970e+54.2971e+5

4.681400e+64.681405e+64.681410e+64.681415e+64.681420e+64.681425e+64.681430e+64.681435e+6

Z Data

X Data

Y Data

ACFL

9 values that divide sorted data into 10 equal parts with each part representing1/10th of the sample or population

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Concept of Canopy Density Metrics

0

5

10

15

20

25

30

4.2966e+54.2967e+5

4.2968e+54.2969e+5

4.2970e+54.2971e+5

4.681400e+64.681405e+64.681410e+64.681415e+64.681420e+64.681425e+64.681430e+64.681435e+6

Z Data

X DataY Data

ACFL

P0

heig

ht

1

D(%)

• Range of heights divided into 10 equal intervals.

• Cumulative proportion of returns starting from the lowest interval.

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Statistical Analyses

• Linear / Non-Linear Regression

• Model Diagnosis– Test for Normality: Shapiro-Wilks Test– Test for Homoschedasticity: Modified Levene’s Test– Multicollinearity: Variance Inflation Factors (VIF) < 10– Natural Logarithm Transformation

• Validation– Independent data sets or PRESS Procedure

nn xxy ...110

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Workflow – Model Building

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Decimation 0 ~3 p/mDecimation 0 ~3 p/m22 Decimation 1 ~1.6 p/mDecimation 1 ~1.6 p/m22 Decimation 2 ~0.5 p/mDecimation 2 ~0.5 p/m22

What is the appropriate density to acquireWhat is the appropriate density to acquireLiDAR at for stand level prediction?LiDAR at for stand level prediction?

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Decimation 0 ~3 p/mDecimation 0 ~3 p/m22 Decimation 1 ~1.6 p/mDecimation 1 ~1.6 p/m22 Decimation 2 ~0.5 p/mDecimation 2 ~0.5 p/m22

Cumulative Proportion of LiDAR Returnsfor Level of LiDAR Decimation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

2 4 6 8 10 12 14 16 18 20 22 24 26

Height Class

%D0D1D2

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Study Sites

• Three (3) main Ontario study sites:– Swan Lake (SL) Reserve (n=32)– Petawawa Research Forest (PRF) (n=32)– Romeo Mallette (RM) Forest (n=130)

Jack PineJack Pine Black SpruceBlack SpruceGrtGrt LksLks PinePine

TolerantTolerant HwdsHwds IntolerantIntolerant HwdsHwds MixedwoodsMixedwoods

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LiDAR DataPeriod: Summer 2007Sensor: Optech ALTM 3100Altitude: 1000 mOverlap: N/ASpeed: 120 knotsSystem PRF: 100 kHzScan Freq: 54 HzScan Half Angle: 13°Cross Track Resolution: 0.499 mDown Track Resolution: 0.572 m

~ 3 point/m2

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WorkflowDecimate LiDAR Data~3 pulses/m2

~1.6 pulses/m2

~.5 pulses/m2

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Petawawa Research Forest – Grt. Lakes Pine(Pw/Pr/Pj Natural + Planted + Thinned)

Variable Decimation Level 0 Decimation Level 1 Decimation Level 2

R2 RMSE(%)

R2 RMSE(%)

R2 RMSE(%)

SUMBA .89 4.8(13.3)

.89 4.7(13.2)

.88 4.9(13.7)

SUMGTV .93 56.1(13.4)

.94 51.2(12.3)

.92 60.0(14.4)

DENSITY .74 207.8(34.7)

.72 214.9(35.8)

.68 226.9(37.8)

QMDBH .87 3.9(12.7)

.86 4.0(12.9)

.87 3.8(12.3)

AVGHT .94 1.3(5.7)

.94 1.3(6.0)

.94 1.3(5.7)

TOPHT .95 1.2(4.4)

.95 1.2(4.5)

.94 1.3(4.7)

SUMBIO .74 29,818(21.0)

.76 28,743(20.3)

,78 27,572(19.4)

Current StatusPreliminary Results

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Variable Decimation Level 0 Decimation Level 1 Decimation Level 2

R2 RMSE(%)

R2 RMSE(%)

R2 RMSE(%)

SUMBA .49 2.7(10.6)

.49 2.7(10.7)

.58 2.4(9.7)

SUMGTV .59 25.4(11.2)

.60 24.9(11.0)

.61 24.8(11.0)

DENSITY .84 42.8(10.5)

.86 39.8(9.8)

.83 43.7(10.7)

QMDBH .69 2.0(7.3)

.72 1.9(7.0)

.70 2.0(7.1)

AVGHT .84 0.6(3.4)

.84 0.6(3.4)

.85 0.6(3.2)

TOPHT .82 0.7(3.0)

.85 0.7(2.8)

.86 0.7(2.7)

SUMBIO .46 24,795(12.6)

.50 23,811(12.1)

.58 23,811(12.1)

Tolerant Hardwoods

Current StatusPreliminary Results~ 3 pts/m2 ~ 1.6 pts/m2 ~ 0.4 pts/m2

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Romeo Malette Forest – Mixedwoods

Preliminary ResultsDensity Basal Area

Dbhq GTV Biomass

Mixedwoods - Decimation Level 0

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

Measued BA m2/ha

Pred

icte

d BA

m2/

ha

Mixedwoods - Devimation Level 0

-100

0

100

200

300

400

500

600

700

800

0 100 200 300 400 500 600 700 800

Measured GTV m3/ha

Pred

icte

d G

TV m

3/ha


0

5

10

15

20

25

30

35

0 5 10 15 20 25 30 35

Measured Dbhq cm

Pred

icte

d D

bhq

cm


0

50000

100000

150000

200000

250000

300000

350000

0 50000 100000 150000 200000 250000 300000 350000

Measured Biomass Kg/ha

Pred

icte

d Bi

omas

s Kg

/ha

Top Height


0

5

10

15

20

25

30

35

40

0 5 10 15 20 25 30 35 40

Measured Top Height m

Pred

icte

d To

p H

eigh

t m


0

500

1000

1500

2000

2500

3000

0 500 1000 1500 2000 2500 3000

Measured Density Stems/ha

Pred

icte

d D

ensi

ty S

tem

s/ha

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Romeo Malette Forest – MixedwoodsVariable Decimation Level 0 Decimation Level 1 Decimation Level 2

R2 RMSE(%)

R2 RMSE(%)

R2 RMSE(%)

SUMBA .72 5.5(16.8)

.73 5.3(16.4)

.72 5.5(16.8)

SUMGTV .82 48.2(18.2)

.83 47.1(17.8)

.82 48.2(18.2)

DENSITY .12 309.4(28.1)

.11 310.9(28.2)

.12 309.4(28.1)

QMDBH .61 1.6(8.5)

.62 1.6(8.4)

.61 1.6(8.5)

AVGHT .78 0.9(6.0)

.82 0.8(5.3)

.78 0.9(6.0)

TOPHT .97 0.6(2.8)

.97 0.6(2.8)

.97 0.6(2.8)

SUMBIO .75 22,510(16.2)

.81 19,469(14.1)

.75 22,510(16.25)

Current Status

This analysis: All Returns – No Z Threshold imposed

Preliminary Results

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Applying Models to the LandscapeLiDAR Predictor Surfaces

•Each surfacecorresponds to a LIDARpredictor

•Example is 12,000 ha ofPetawawa ResearchForest

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Image based Interpretation

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Image based Interpretation – Polygon Mask

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LiDAR derived Canopy Height Model

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Operational LiDAR Enhancements

Stratify by:

•Forest type

•Forest unit

•etc…

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Operational LiDAR Enhancements

Not to scale – illustrative purposes only

400m400m22 Predictive Grid CellsPredictive Grid Cells

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Operational LiDAR EnhancementsAverage Height (m) 400mAverage Height (m) 400m22 RasterRaster

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54


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Operational LiDAR EnhancementsBasal Area (mBasal Area (m22/ha) 400m/ha) 400m22 RasterRaster

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Operational LiDAR EnhancementsGTV (mGTV (m33/ha) 400m/ha) 400m22 RasterRaster

Dbhq (cm) 400mDbhq (cm) 400m22 RasterRaster

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Diameter Distribution Modelling

From: Quantifying diameter distributions of uneven-aged toleranthardwood stands using LiDAR

Jones, T, Woods, M. and Lim K 2009 Inpress Silvilaser 2009

Sawtimber size class

pole small medium large

Basal area (m2 ha -1)

0

2

4

6

8

10

Observed basal areaLiDAR predicted basal area

a

b

aa

aa

aa

SLFRR validation plots

0.00

0.05

0.10

0.15

0.20

0.25

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 800.00

0.05

0.10

0.15

0.20

0.25

0.00

0.05

0.10

0.15

0.20

0.25Observed diameter distributionLiDAR estimated

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 800.00

0.05

0.10

0.15

0.20

0.25

Relative Frequency

Relative Frequency

Diameter class (cm)

Validation Data

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All Rondeau Vegegation PlotsComparing LiDAR against Ground Measured Heights (Bird Monitoring Classes)

0

5

10

15

20

25

30

0-50 .51-1.30 1.31-3.0 3.1-6.0 6.1-10.0 10.1-20.0 20+

Height Class

Heig

ht m First Returns

All_ReturnsMeasured HT

LiDAR unlocking Vertical Structure for Habitat ModelingLiDAR unlocking Vertical Structure for Habitat Modeling

Percent Vegetation Returns

0 5 10 15 20 25 30 35 40 45

.51-1.3

1.31-3.0

3.0-6.0

6-10

10.1-20

20+

101C

Percent Vegetation Returns

0 5 10 15 20 25 30 35 40 45

.51-1.3

1.31-3.0

3.0-6.0

6-10

10.1-20

20+

105A

% Vegetation Cover by Height Class

Canopy Height Model Sliced Canopy Height Model

Foliage Height Diversity

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0

5

10

15

20

25

430015 430020 430025 430030 430035 430040 430045 430050 430055 430060

Z

102C

0

5

10

15

20

25

30

430285 430290 430295 430300 430305 430310 430315 430320 430325 430330

Z

Vertical Distribution Ratio = 0.64Vertical Distribution Ratio = 0.64Coefficient of Variation = 56.19Coefficient of Variation = 56.19

Vertical Distribution Ratio = 0.51Vertical Distribution Ratio = 0.51Coefficient of Variation = 39.95Coefficient of Variation = 39.95

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The Role of MultibandThe Role of Multiband Orthophotography in theOrthophotography in theproduction of Semiproduction of Semi--Automated Enhanced ForestAutomated Enhanced Forest

Inventories in the Great Lakes St. Lawrence ForestInventories in the Great Lakes St. Lawrence Forest

Enhanced Forest Productivity FundEnhanced Forest Productivity Fund

Murray Woods, Dave NesbittMurray Woods, Dave NesbittSouthern Science & InformationSouthern Science & Information

FranFranççoisois Gougeon, Don LeckieGougeon, Don LeckieCanadian Forest ServiceCanadian Forest Service

Paul Courville, Sue PickeringPaul Courville, Sue PickeringForest Research PartnershipForest Research Partnership

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Project Focus

Explore the potential of:• Using high-resolution multi-band

digital imagery for automatedinventory production– Stand Level– Tree Level

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Working with Drs GougeonWorking with Drs Gougeonand Leckie at CFSand Leckie at CFS-- PFCPFC ––application evolutionapplication evolution

Working with SilvatechWorking with SilvatechConsulting Ltd.Consulting Ltd. ––operational applicationoperational application

RESEARCH

OPERATIONS

SemiSemi--automatedautomated -- Individual Tree Crown ClassificationIndividual Tree Crown Classification

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SemiSemi--automatedautomated -- Individual Species ClassificationIndividual Species Classification

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RGB

Spectral-Based Masking

Lidar-Based Masking

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RGB


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RGB


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68


RGB


Ground trainingGround training

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RGB


Soft copy trainingSoft copy training

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RGB


Green and Blue by species and 0.7 SD of Green

Pw im

Pw o

Pw br

Mh

Ms

Bwd

Bw

AtAtpq

Alt

By

La

Pr

Sw

Sb

Pj BD

Bf

110

120

130

140

150

160

170

130 140 150 160 170 180

Intensity of Green

Inte

nsity

of B

lue

Pw imPw oldPw brMhMsBwdBwAtAtpqAltByLaPrSwSbPjBDBf

nIR and Red by species and 0.7 SD of nIR

Pw im

Pw old

Pw br

Mh

Ms

Bwd

Bw

At

At p Alt

By

La

Pr

Sw

Sb

Pj

BDBf

120

130

140

150

160

170

160 170 180 190 200 210 220

Intensity of near Infrared

Inte

nsity

of R

ed


Species Separability

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nIR and Red by species and 0.7 SD of nIR

Pw im

Pw old

Pw br

Mh

Ms

Bwd

Bw

At

At p Alt

By

La

Pr

Sw

Sb

Pj

BDBf

120

130

140

150

160

170

160 170 180 190 200 210 220

Intensity of near Infrared

Inte

nsity

of R

ed


ITC Species Signatures in Multispectral Space for PRF

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73


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Pw Mh Ms Bw As La Pr Sw Sb PjPw 54.19 26.23 0.00 0.00 0.00 12.44 8.87 0.78 3.83 2.17Mh 2.86 66.12 0.00 0.00 0.00 0.46 0.00 0.52 0.18 0.00Ms 2.66 0.00 66.67 0.00 9.24 0.00 9.61 0.00 0.00 0.00Bw 0.00 0.00 0.00 96.77 9.24 0.00 0.18 0.00 0.00 0.00As 3.27 0.55 33.33 3.23 75.63 5.53 6.47 0.00 0.00 0.00La 14.72 3.83 0.00 0.00 0.00 73.27 0.55 0.00 1.28 0.00Pr 11.25 0.55 0.00 0.00 4.20 4.15 68.21 2.08 0.00 4.35Sw 0.41 0.00 0.00 0.00 0.00 0.00 0.92 67.79 5.28 2.17Sb 6.75 0.00 0.00 0.00 0.00 0.46 0.00 10.91 83.97 1.09Pj 2.45 0.00 0.00 0.00 0.00 0.92 3.51 15.06 2.19 86.96Un 1.43 2.73 0.00 0.00 1.68 2.76 1.66 2.86 3.28 3.26

# ITCs 489 183 18 31 119 217 541 385 549 92

Confusion Matrix Results for PRF

Overall accuracy (precision) of 70.4%

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76


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Automated Polygon DelineationAutomated Polygon Delineationwith Species Classificationwith Species Classification

49 Pw 21 Sw 9 Bf 8 Sb 7 Bd 3 Pr 3 Ce31m

42% Crown Closure238 Stems

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Semi-Automated Imagery Classificationand LiDAR for Inventory Production

• Semi-automated imagery classification is a maturing area –focus of ongoing national research efforts with CWFC

• Boreal forest species currently best opportunity to benefitfrom imagery analysis techniques– Another “tool” for an interpreter– Objective vs. Subjective approach– Able to process large areas very quickly– Ability to improve and recompile– Driving towards tree level inventories– Hardwood aggregation or over splitting

receiving attention

Romeo Malette Forest Example

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Semi-Automated Imagery Classificationand LiDAR for Inventory Production

• LiDAR methods well understood and ready to operationallyimplement– Become an affordable technology– Demonstrating that higher sampling point densities are not required

for better estimates of forest inventory variables.– Current inventory efforts on a 650K ha NE Ontario Forest– Planned inventory project for 1.3 Million ha forest in 2010– Need to continue to expand the areas in which LiDAR has the

potential to help add value to inventories – eg. Stream prediction,forest habitat modeling, renewable energy modeling

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In Conclusion, I believe that…These technologies offergreat potential to improvecurrent methods ofinventory production.Sound sustainablemanagement andbusiness decisionsrequire detailed, highquality & spatially explicitresource inventoryinformation…we can’tkeep putting it off.

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Ministry of Natural Resources

Author: Mike Baldwin

Thank You

Questions

Contact

Murray Woods at:

[email protected]

Semi-Automated Imagery Analysis and LiDAR …€¦ · 6 What is LiDAR? Light Detection And Ranging...

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Transcript of Semi-Automated Imagery Analysis and LiDAR …€¦ · 6 What is LiDAR? Light Detection And Ranging...